Hpgltools examples using a small bacterial data set.

1 Introduction

hpgltools was written to make working with high-throughput data analyses easier. These analyses generally fall into a few stages:

1. Data visualization and outlier/batch evaluation
2. Differential expression analyses

1. Visualization and export of these results

3. Gene ontology/KEGG analyses

1. Visualization and export of these results

4. Genome visualizations

1. With circos
2. With genoplotR

Before any of these tasks may be performed, the data must be loaded into memory. hpgltools attempts to make this easier with create_expt() and subset_expt().

2 Loading (meta)data and annotations

The following examples will use a real data set from a recent experiment in our lab. The raw data was processed using a mix of trimmomatic, biopieces, bowtie, samtools, and htseq. The final count tables were deposited into the ‘preprocessing/count_tables/’ tree. The resulting data structure was named ‘most_v0M1,’ named because it is comprised of count tables with 0 mismatches and 1 randomly-placed multi-match.

The annotation file was mgas_5005.gff.xz resides in ‘reference/gff/’.

The count tables and meta-data were loaded through the create_expt() function and the genome annotations were loaded with gff2df().

library(hpgltools)
data_file <- system.file("cdm_expt.rda", package="hpgltools")
cdm <- new.env()
load(data_file, envir=cdm)
rm(data_file)

ls()

## [1] "cdm"        "hh"         "ht"         "rmd_file"   "setnicepar"

Two variables should exist now: rmd_file in case I want to knitr this file, cdm which is a list including the data required to make an expressionset. Using this information, I can create an expressionset.

expt <- create_expt(count_dataframe=cdm$cdm_counts, metadata=cdm$cdm_metadata, gene_info=cdm$gene_info)

## Bringing together the count matrix and gene information.

## Some annotations were lost in merging, setting them to 'undefined'.

knitr::kable(head(expt$design))

	sampleid	type	stage	replicate	mutantname	media	exptdate	libdate	batch	condition	readspassed	ncrna	xncrna	remaining	genome	xgenome	otherbacterial	xother	colors	counts	intercounts	design	file
HPGL0418	HPGL0418	WT	LL	1	5448-1	CDMg	20130430	20140409	a	wt_ll_cg	17425675	350440	2.01%	17075235	16061860	94.07%	356542	2.09%	#E12C8C	processed_data/count_tables/HPGL0418/HPGL0418_forward-trimmed-v1M1l20.count.xz	unknown	a	null
HPGL0419	HPGL0419	WT	LL	2	5448-2	CDMg	20130430	20140409	b	wt_ll_cg	18106361	398984	2.20%	17707377	16619183	93.85%	392803	2.22%	#E12C8C	processed_data/count_tables/HPGL0419/HPGL0419_forward-trimmed-v1M1l20.count.xz	unknown	b	null
HPGL0420	HPGL0420	mga	LL	1	Mga-1	CDMg	20130430	20140409	a	mga1_ll_cg	20499936	417440	2.04%	20082496	18062942	89.94%	416260	2.07%	#BE5067	processed_data/count_tables/HPGL0420/HPGL0420_forward-trimmed-v1M1l20.count.xz	unknown	a	null
HPGL0421	HPGL0421	mga	LL	2	Mga-2	CDMg	20130430	20140409	b	mga1_ll_cg	17216381	397021	2.31%	16819360	14795703	87.97%	376295	2.24%	#BE5067	processed_data/count_tables/HPGL0421/HPGL0421_forward-trimmed-v1M1l20.count.xz	unknown	b	null
HPGL0422	HPGL0422	WT	LL	1	5448-1	CDMf	20130430	20140415	a	wt_ll_cf	17112706	367791	2.15%	16744915	15114930	90.27%	367974	2.20%	#8E7E40	processed_data/count_tables/HPGL0422/HPGL0422_forward-trimmed-v1M1l20.count.xz	unknown	a	null
HPGL0423	HPGL0423	WT	LL	2	5448-2	CDMf	20130430	20140415	b	wt_ll_cf	18782729	395748	2.11%	18386981	17330239	94.25%	386443	2.10%	#8E7E40	processed_data/count_tables/HPGL0423/HPGL0423_forward-trimmed-v1M1l20.count.xz	unknown	b	null

summary(expt)

##                        Length Class              Mode     
## title                   1     -none-             character
## notes                   1     -none-             character
## initial_metadata       23     data.frame         list     
## expressionset           1     ExpressionSet      S4       
## design                 23     data.frame         list     
## conditions              8     -none-             character
## batches                 8     -none-             character
## samplenames             8     -none-             character
## colors                  8     -none-             character
## state                   5     -none-             list     
## original_expressionset  1     ExpressionSet      S4       
## original_libsize        8     -none-             numeric  
## libsize                 8     -none-             numeric  
## original_metadata       1     AnnotatedDataFrame S4

The data structure generated by create_expt() is a list containing the following slots:

• initial_metadata: A backup of the metadata
• original_expressionset: A backup of the raw counts
• expressionset: The current count data
• samples: A data frame of metadata used for subsets
• design: The design of the experiment
• definitions: Extended design information, these are probably redundant and should be pruned.
• stages: The experimental stage
• types: Cell types
• conditions: Experimental condition
• batches: Experimental batch
• samplenames: Names of the samples
• colors: Colors chosen for graphs and such
• names: Bringing together the condition/batch
• filtered: low-count filtering status of the counts
• transform: transformation applied to the counts
• norm: normalization applied to the counts
• convert: cpm/rpkm/etc applied to the data
• original_libsize: the library sizes before normalization
• columns: A backup of the sample names

The primary reason I created the expt object was that I did not fully understand how expressionSets worked. From my perspective, the documentation was rather opaque and therefore I decided to create a simpler version of the expressionset. As I learned how to manipulate S4 classes more efficiently, I gradually began to realize that they are not so bad. Nonetheless, I found it nice to be able to keep some extra state information with my expressionsets, along with a backup copy in case of mistakes, and some easier ways to extract information like conditions/batches/colors/etc. Thus my *expt() functions grew into wrappers to call the native functions for manipulating expressionsets.

3 Raw metrics

With the above in mind, once we have the various metadata and count data loaded into memory, a most common next step is examine the data before molesting it:

raw_metrics <- sm(graph_metrics(expt, qq=TRUE))

The function graph_metrics() performs all of the likely plots one might want. Some of which are not really appropriate for non-normalized data unless it is incredibly well behaved (after 30 years, I still want to spell behaved ‘behaived’, why is that?).

## View a raw library size plot
raw_metrics$libsize

## Or boxplot to see the data distribution
raw_metrics$boxplot

## The warning is because it automatically uses a log scale and there are some 0 count genes.
## Perhaps you prefer density plots
raw_metrics$density

## quantile/quantile plots compared to the median of all samples
raw_metrics$qqrat

## Here we can see some samples are differently 'shaped' compared to the median than others
## There are other plots one may view, but this data set is a bit too crowded as is.
## The following summary shows the other available plots:
summary(raw_metrics)

##           Length Class        Mode   
## nonzero   9      gg           list   
## libsize   9      gg           list   
## boxplot   9      gg           list   
## corheat   3      recordedplot list   
## smc       9      gg           list   
## disheat   3      recordedplot list   
## smd       9      gg           list   
## pcaplot   9      gg           list   
## pcatable  8      data.frame   list   
## pcares    4      data.frame   list   
## pcavar    7      -none-       numeric
## tsneplot  9      gg           list   
## tsnetable 8      data.frame   list   
## tsneres   4      -none-       list   
## tsnevar   8      -none-       numeric
## density   9      gg           list   
## legend    2      -none-       list   
## qqlog     3      recordedplot list   
## qqrat     3      recordedplot list   
## ma        0      -none-       NULL

The plots are all generated by calling plot_something() where the somethings are:

• nonzero: scatter plot of number of non-zero genes with respect to CPM pseudocounts.
• libsize: bar plot of the (pseudo) counts in annotated features observed by sample.
• boxplot: boxplot describing the distribution of counts with respect to features by sample.
• corheat: correlation heat map describing the relative similarities among samples.
• smc: ‘standard median correlation’, take the pairwise correlations of all samples, calculate the medians, and look for a single sample with significantly lower correlations (falling below the red line).
• disheat: distance heat map. As above but with a distance metric.
• smd: ‘standard median distance’, as above but using the distance metrics and a line above.
• pcaplot: plot_pca() gives many outputs, including a PCA plot of the first 2 principal components.
• pcatable: and a table describing the same metadata.
• pcares: along with the table of variance, cumulative variance, condition/batch r^2.
• pcavar: and the variance by component observed.
• density: pretty much the exact same thing as the boxplot above, but as a density plot.
• legend: a convenient legend for figures and such.
• qqlog: qq-plots of each sample vs. the median on the log scale.
• qqrat: qq-plots of each sample vs. the median as ratios.
• ma: bland-altman plots of each sample of M(log abundance) with respect to A(mean average).

4 Subsetting data

On the other hand, we might take a subset of the data to focus on the late-log vs. early-log samples.

The expt_subset() function allows one to pull material from the experimental design.

Once we have a smaller data set, we can more easily use PCA to see how the sample separate.

head(expt$design)

##          sampleid type stage replicate mutantname media exptdate  libdate
## HPGL0418 HPGL0418   WT    LL         1     5448-1  CDMg 20130430 20140409
## HPGL0419 HPGL0419   WT    LL         2     5448-2  CDMg 20130430 20140409
## HPGL0420 HPGL0420  mga    LL         1      Mga-1  CDMg 20130430 20140409
## HPGL0421 HPGL0421  mga    LL         2      Mga-2  CDMg 20130430 20140409
## HPGL0422 HPGL0422   WT    LL         1     5448-1  CDMf 20130430 20140415
## HPGL0423 HPGL0423   WT    LL         2     5448-2  CDMf 20130430 20140415
##          batch  condition readspassed  ncrna xncrna remaining   genome
## HPGL0418     a   wt_ll_cg    17425675 350440  2.01%  17075235 16061860
## HPGL0419     b   wt_ll_cg    18106361 398984  2.20%  17707377 16619183
## HPGL0420     a mga1_ll_cg    20499936 417440  2.04%  20082496 18062942
## HPGL0421     b mga1_ll_cg    17216381 397021  2.31%  16819360 14795703
## HPGL0422     a   wt_ll_cf    17112706 367791  2.15%  16744915 15114930
## HPGL0423     b   wt_ll_cf    18782729 395748  2.11%  18386981 17330239
##          xgenome otherbacterial xother  colors
## HPGL0418  94.07%         356542  2.09% #E12C8C
## HPGL0419  93.85%         392803  2.22% #E12C8C
## HPGL0420  89.94%         416260  2.07% #BE5067
## HPGL0421  87.97%         376295  2.24% #BE5067
## HPGL0422  90.27%         367974  2.20% #8E7E40
## HPGL0423  94.25%         386443  2.10% #8E7E40
##                                                                                  counts
## HPGL0418 processed_data/count_tables/HPGL0418/HPGL0418_forward-trimmed-v1M1l20.count.xz
## HPGL0419 processed_data/count_tables/HPGL0419/HPGL0419_forward-trimmed-v1M1l20.count.xz
## HPGL0420 processed_data/count_tables/HPGL0420/HPGL0420_forward-trimmed-v1M1l20.count.xz
## HPGL0421 processed_data/count_tables/HPGL0421/HPGL0421_forward-trimmed-v1M1l20.count.xz
## HPGL0422 processed_data/count_tables/HPGL0422/HPGL0422_forward-trimmed-v1M1l20.count.xz
## HPGL0423 processed_data/count_tables/HPGL0423/HPGL0423_forward-trimmed-v1M1l20.count.xz
##          intercounts design file
## HPGL0418     unknown      a null
## HPGL0419     unknown      b null
## HPGL0420     unknown      a null
## HPGL0421     unknown      b null
## HPGL0422     unknown      a null
## HPGL0423     unknown      b null

## elt stands for: "early/late in thy"
batch_a <- expt_subset(expt, subset="batch=='a'")
batch_b <- expt_subset(expt, subset="batch=='b'")

a_metrics <- graph_metrics(batch_a)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 475 zero count features.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## There is just one batch in this data.

## Graphing a T-SNE plot.

## There is just one batch in this data.

## Plotting a density plot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 475 zero count features.

## Printing a color to condition legend.

b_metrics <- graph_metrics(batch_b)

## Graphing number of non-zero genes with respect to CPM by library.

## Graphing library sizes.

## Graphing a boxplot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 448 zero count features.

## Graphing a correlation heatmap.

## Graphing a standard median correlation.

## Performing correlation.

## Graphing a distance heatmap.

## Graphing a standard median distance.

## Performing distance.

## Graphing a PCA plot.

## There is just one batch in this data.

## Graphing a T-SNE plot.

## There is just one batch in this data.

## Plotting a density plot.

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

## Some entries are 0.  We are on log scale, setting them to 0.5.

## Changed 448 zero count features.

## Printing a color to condition legend.

a_metrics$pcaplot

b_metrics$pcaplot

a_metrics$tsneplot

b_metrics$tsneplot

5 Normalizing data

It is pretty obvious that the raw data is a bit jumbled according to PCA. This is not paricularly suprising since we didn’t normalize it at all. Therefore, in this block I will normalize it a few ways and follow up with some visualizations of showing how the apparent relationships change in the data.

## doing nothing to the data except log2 transforming it has a surprisingly large effect
norm_test <- normalize_expt(expt, transform="log2")

## This function will replace the expt$expressionset slot with:

## log2(data)

## It backs up the current data into a slot named:
##  expt$backup_expressionset. It will also save copies of each step along the way
##  in expt$normalized with the corresponding libsizes. Keep the libsizes in mind
##  when invoking limma.  The appropriate libsize is the non-log(cpm(normalized)).
##  This is most likely kept at:
##  'new_expt$normalized$intermediate_counts$normalization$libsizes'
##  A copy of this may also be found at:
##  new_expt$best_libsize

## Filter is false, this should likely be set to something, good
##  choices include cbcb, kofa, pofa (anything but FALSE).  If you want this to
##  stay FALSE, keep in mind that if other normalizations are performed, then the
##  resulting libsizes are likely to be strange (potentially negative!)

## Leaving the data unconverted.  It is often advisable to cpm/rpkm
##  the data to normalize for sampling differences, keep in mind though that rpkm
##  has some annoying biases, and voom() by default does a cpm (though hpgl_voom()
##  will try to detect this).

## Leaving the data unnormalized.  This is necessary for DESeq, but
##  EdgeR/limma might benefit from normalization.  Good choices include quantile,
##  size-factor, tmm, etc.

## Not correcting the count-data for batch effects.  If batch is
##  included in EdgerR/limma's model, then this is probably wise; but in extreme
##  batch effects this is a good parameter to play with.

## Step 1: not doing count filtering.

## Step 2: not normalizing the data.

## Step 3: not converting the data.

## Step 4: transforming the data with log2.

## transform_counts: Found 923 values equal to 0, adding 1 to the matrix.

## Step 5: not doing batch correction.

l2_metrics <- sm(graph_metrics(norm_test))

## a quantile normalization alone affect some, but not all of the data
norm_test <- sm(normalize_expt(expt, norm="quant"))
q_metrics <- sm(graph_metrics(norm_test))  ## q for quant, oh oh oh!

## cpm alone brings out some samples, too
norm_test <- sm(normalize_expt(expt, convert="cpm"))
c_metrics <- sm(graph_metrics(norm_test))  ## c for cpm!

## low count filtering has some effect, too
norm_test <- sm(normalize_expt(expt, filter="pofa"))
f_metrics <- sm(graph_metrics(norm_test))  ## f for filter!

## how about if we mix and match methods?
norm_test <- sm(normalize_expt(expt, transform="log2", convert="cpm",
                               norm="quant", batch="combat_scale", filter=TRUE,
                               batch_step=4, low_to_zero=TRUE))
## Some metrics are not very useful on (especially quantile) normalized data
norm_graphs <- sm(graph_metrics(norm_test))

Now lets see some of the resulting metrics, in this case I will just compare some pca plots, as they are good at fooling our silly visual brains into seeing patterns.

l2_metrics$pcaplot

## Also viewable with plot_pca()$plot
## PCA plots seem (to me) to prefer log2 scale data.
q_metrics$pcaplot

## only normalizing on the quantiles leaves the data open to scale effects.
c_metrics$pcaplot

## but cpm alone is insufficient
f_metrics$pcaplot

## only filtering out low-count genes is helpful as well
norm_graphs$pcaplot

## The different batch effect testing methods have a pretty widely ranging effect on the clustering
## play with them by changing the batch= parameter to:
## "limma", "sva", "svaseq", "limmaresid", "ruvg", "combat", combatmod"
knitr::kable(norm_graphs$pcares)

propVar	cumPropVar	cond.R2	batch.R2
82.42	82.42	99.36	0.00
6.39	88.81	86.57	1.80
4.02	92.83	49.64	1.55
2.80	95.63	8.87	17.53
2.11	97.74	48.94	6.99
1.27	99.01	6.16	68.83
0.99	100.00	0.45	3.31

## Thus we see a dramatic decrease in variance accounted for
## by batch after applying limma's 'removebatcheffect'
## (see batch.R2 here vs. above)
norm_graphs$smc

norm_graphs$disheat  ## svaseq's batch correction seems to draw out the signal quite nicely.

## It is worth noting that the wt, early log, thy, replicate c samples are still a bit weird.
norm_graphs$tsneplot

6 Performing DE analyses

This is a relatively small data set, so performing some differential expression analyses really should not take long at all.

When performing these analyses with hpgltools, it will attempt to perform similar analyses with limma, edgeR, and DESeq2 via the all_pairwise() function. The most likely argument is ‘model_batch’ which may be used to explicitly include/exclude a batch factor in the model, or ask it to attempt including batch factors from sva/ruv/etc. By default it will attempt to include a column from the experimental design named ‘batch’.

spyogenes_de <- sm(all_pairwise(expt))

## Even the lowest correlations are quite high.

The result of all_pairwise() is a list of the results from limma, edger, and deseq. In addition, I implemented a very simplistic, differential expression function named ‘basic()’. It also provides some simple measurements of how well the various analyses agree (ergo the black and white heatmap).

Working with these separate tables can be more than a little annoying, combine_de_tables() attempts to simplify this. It will bring together the various tables, and if asked attempt to bring them together into a pretty-ified excel workbook.

all_pairwise() arbitrarily performs all possible pairwise comparisons. This is not necessarily what one actually wishes to see. Therefore, the argument keepers takes a list of contrasts:

my_keepers <- list(
    "wt_media" = c("wt_ll_cf", "wt_ll_cg"),
    "mga_media" = c("mga_ll_cf", "mga_ll_cg"))

In the above example, if the keepers argument to combine_de_tables() is given as my_keepers, then the resulting table will not have the set of 6 possible comparisons, but instead will only have 2 tables named ‘wt_media’ and ‘mga_media’, which if printed to excel will be sheets named accordingly.

Because these tables can get quickly extremely large, the excel workbooks may become too big for the zip(1) program to properly merge. If that happens, one may either remove some(or all) of the plots and/or have it print csv versions of the same tables.

spyogenes_tables <- sm(combine_de_tables(spyogenes_de, excel=FALSE))
summary(spyogenes_tables)

##             Length Class      Mode
## data         6     -none-     list
## limma_plots  6     -none-     list
## edger_plots  6     -none-     list
## deseq_plots  6     -none-     list
## comp_plot    0     -none-     list
## venns        0     -none-     list
## de_summary  22     data.frame list

## Try changing the p-adjustment
spyogenes_tables <- sm(combine_de_tables(spyogenes_de, excel=FALSE, padj_type="BH"))
head(spyogenes_tables$data[[1]])

##                seqnames start  end width strand source type            id
## M5005_Spy0001 NC_007297   202 1557  1356      + RefSeq gene M5005_Spy0001
## M5005_Spy0002 NC_007297  1712 2848  1137      + RefSeq gene M5005_Spy0002
## M5005_Spy0003 NC_007297  2923 3120   198      + RefSeq gene M5005_Spy0003
## M5005_Spy0004 NC_007297  3450 4565  1116      + RefSeq gene M5005_Spy0004
## M5005_Spy0005 NC_007297  4635 5204   570      + RefSeq gene M5005_Spy0005
## M5005_Spy0006 NC_007297  5207 8710  3504      + RefSeq gene M5005_Spy0006
##                       dbxref gbkey           name          note      gene
## M5005_Spy0001 GeneID:3571011  Gene           dnaA M5005_Spy0001      dnaA
## M5005_Spy0002 GeneID:3571012  Gene           dnaN M5005_Spy0002      dnaN
## M5005_Spy0003 GeneID:3571013  Gene M5005_Spy_0003 M5005_Spy0003 undefined
## M5005_Spy0004 GeneID:3571014  Gene           ychF M5005_Spy0004      ychF
## M5005_Spy0005 GeneID:3571015  Gene            pth M5005_Spy0005       pth
## M5005_Spy0006 GeneID:3571016  Gene           trcF M5005_Spy0006      trcF
##                     locustag       parent  genesynonym limma_logfc
## M5005_Spy0001 M5005_Spy_0001 character(0) character(0)     0.22320
## M5005_Spy0002 M5005_Spy_0002 character(0) character(0)     0.17990
## M5005_Spy0003 M5005_Spy_0003 character(0) character(0)    -0.06607
## M5005_Spy0004 M5005_Spy_0004 character(0) character(0)     0.06423
## M5005_Spy0005 M5005_Spy_0005 character(0) character(0)     0.07449
## M5005_Spy0006 M5005_Spy_0006 character(0) character(0)    -0.11200
##               limma_adjp deseq_logfc deseq_adjp edger_logfc edger_adjp
## M5005_Spy0001     0.5637     0.22190     0.6396     0.23690     0.6469
## M5005_Spy0002     0.7002     0.15950     0.7992     0.17470     0.7889
## M5005_Spy0003     0.9421    -0.04509     0.9705    -0.03071     1.0000
## M5005_Spy0004     0.9447     0.05406     0.9578     0.06851     0.9715
## M5005_Spy0005     0.9169     0.05262     0.9646     0.06755     0.9980
## M5005_Spy0006     0.6719    -0.09324     0.8751    -0.07858     0.9398
##               limma_ave limma_t limma_b limma_p deseq_basemean deseq_lfcse
## M5005_Spy0001     9.525  1.3760  -6.173  0.2286         8667.0      0.2008
## M5005_Spy0002     9.621  0.9257  -6.657  0.3981         9174.0      0.2241
## M5005_Spy0003     5.855 -0.3763  -6.771  0.7225          699.7      0.2303
## M5005_Spy0004    10.060  0.3657  -7.081  0.7299        12590.0      0.2149
## M5005_Spy0005     6.452  0.4288  -6.768  0.6863         1017.0      0.2201
## M5005_Spy0006     9.907 -0.9875  -6.613  0.3698        11000.0      0.1784
##               deseq_stat deseq_p edger_logcpm edger_lr edger_p
## M5005_Spy0001     1.1050  0.2691        9.562  1.48300  0.2234
## M5005_Spy0002     0.7119  0.4765        9.643  0.71140  0.3990
## M5005_Spy0003    -0.1958  0.8448        5.935  0.01412  0.9054
## M5005_Spy0004     0.2516  0.8014       10.100  0.11900  0.7302
## M5005_Spy0005     0.2391  0.8110        6.473  0.07032  0.7909
## M5005_Spy0006    -0.5226  0.6013        9.904  0.18440  0.6676
##               basic_nummed basic_denmed basic_numvar basic_denvar
## M5005_Spy0001        9.801        9.581    7.389e-02    9.516e-03
## M5005_Spy0002        9.774        9.596    2.584e-02    1.152e-02
## M5005_Spy0003        6.242        6.311    5.987e-02    1.568e-04
## M5005_Spy0004       10.510       10.440    5.725e-02    2.709e-02
## M5005_Spy0005        6.651        6.576    1.134e-02    4.831e-03
## M5005_Spy0006       10.020       10.140    3.987e-02    2.776e-03
##               basic_logfc basic_t   basic_p basic_adjp limma_adjp_bh
## M5005_Spy0001     0.21930 -1.0740 4.471e-01  7.350e-01     5.636e-01
## M5005_Spy0002     0.17810 -1.3030 3.383e-01  6.858e-01     7.002e-01
## M5005_Spy0003    -0.06930  0.4000 7.575e-01  9.025e-01     9.421e-01
## M5005_Spy0004     0.06281 -0.3059 7.918e-01  9.187e-01     9.447e-01
## M5005_Spy0005     0.07502 -0.8344 5.038e-01  7.660e-01     9.169e-01
## M5005_Spy0006    -0.11570  0.7922 5.600e-01  7.986e-01     6.719e-01
##               deseq_adjp_bh edger_adjp_bh basic_adjp_bh  fc_meta    fc_var
## M5005_Spy0001     7.297e-01     6.468e-01     6.791e-01  0.22720 7.468e-06
## M5005_Spy0002     8.853e-01     7.890e-01     6.066e-01  0.17230 2.446e-05
## M5005_Spy0003     1.000e+00     1.000e+00     8.810e-01 -0.05478 6.498e-04
## M5005_Spy0004     1.000e+00     9.715e-01     9.002e-01  0.06218 2.212e-06
## M5005_Spy0005     1.000e+00     9.979e-01     7.182e-01  0.06376 1.499e-05
## M5005_Spy0006     9.540e-01     9.397e-01     7.574e-01 -0.09577 1.045e-03
##               fc_varbymed    p_meta     p_var
## M5005_Spy0001   3.287e-05 2.404e-01 6.260e-04
## M5005_Spy0002   1.420e-04 4.245e-01 2.026e-03
## M5005_Spy0003  -1.186e-02 8.242e-01 8.680e-03
## M5005_Spy0004   3.557e-05 7.538e-01 1.697e-03
## M5005_Spy0005   2.351e-04 7.627e-01 4.483e-03
## M5005_Spy0006  -1.091e-02 5.462e-01 2.445e-02

Finally, extract_significant_genes() may choose ‘significant’ genes based upon a few metrics including z-score vs. the distribution of logFC; a logFC cutoff, (ajusted)p-value cutoff, and/or top/bottom n genes.

spyogenes_sig <- sm(extract_significant_genes(spyogenes_tables, excel=FALSE))
knitr::kable(head(spyogenes_sig$limma$ups[[1]]))

	seqnames	start	end	width	strand	source	type	id	dbxref	gbkey	name	note	gene	locustag	parent	genesynonym	limma_logfc	limma_adjp	deseq_logfc	deseq_adjp	edger_logfc	edger_adjp	limma_ave	limma_t	limma_b	limma_p	deseq_basemean	deseq_lfcse	deseq_stat	deseq_p	edger_logcpm	edger_lr	edger_p	basic_nummed	basic_denmed	basic_numvar	basic_denvar	basic_logfc	basic_t	basic_p	basic_adjp	limma_adjp_bh	deseq_adjp_bh	edger_adjp_bh	basic_adjp_bh	fc_meta	fc_var	fc_varbymed	p_meta	p_var
M5005_Spy1735	NC_007297	1696949	1698145	1197	-	RefSeq	gene	M5005_Spy1735	GeneID:3571136	Gene	speB	M5005_Spy1735	speB	M5005_Spy_1735	character(0)	character(0)	3.509	0.0356	3.691	0	3.696	0	2.346	13.17	1.865	1e-04	83.91	0.4335	8.514	0	2.925	101.70	0	4.349	1.082	3.503e-02	1.818e-02	3.266	-20.02	3.833e-03	6.256e-01	3.555e-02	1.078e-14	6.258e-21	2.895e-02	3.710	1.708e-01	4.602e-02	1.778e-05	9.487e-10
M5005_Spy1575	NC_007297	1535635	1536831	1197	-	RefSeq	gene	M5005_Spy1575	GeneID:3571303	Gene	norA	M5005_Spy1575	norA	M5005_Spy_1575	character(0)	character(0)	2.371	0.0356	2.406	0	2.422	0	6.834	12.96	2.727	1e-04	1849.00	0.2276	10.570	0	7.338	87.72	0	8.250	5.881	3.683e-02	5.610e-02	2.368	-10.99	9.467e-03	6.256e-01	3.555e-02	3.902e-23	4.838e-18	6.812e-02	2.507	8.917e-02	3.557e-02	1.921e-05	1.107e-09
M5005_Spy1715	NC_007297	1675257	1678751	3495	-	RefSeq	gene	M5005_Spy1715	GeneID:3571155	Gene	scpA	M5005_Spy1715	scpA	M5005_Spy_1715	character(0)	character(0)	1.780	0.0359	1.837	0	1.850	0	5.291	11.71	1.906	1e-04	1182.00	0.2667	6.888	0	6.681	39.32	0	3.909	2.155	3.255e-03	1.759e-03	1.754	-35.03	1.314e-03	6.256e-01	3.594e-02	1.553e-09	7.695e-08	1.025e-02	1.820	1.114e-01	6.122e-02	3.110e-05	2.901e-09

7 Make pretty circos graphs

Since most of my circos graphs are for pyogenes, it is likely that the defaults are appropriate for this particular organism.

Much(all) of the following is taken from the material in tests/testthat/test_70mga.R

microbe_ids <- as.character(sm(get_microbesonline_ids("pyogenes MGAS5005")))
mgas_df <- sm(get_microbesonline_annotation(microbe_ids[[1]])[[1]])

## Error in get_microbesonline_annotation(microbe_ids[[1]]): could not find function "get_microbesonline_annotation"

mgas_df$sysName <- gsub(pattern="Spy_", replacement="Spy", x=mgas_df$sysName)

## Error in gsub(pattern = "Spy_", replacement = "Spy", x = mgas_df$sysName): object 'mgas_df' not found

rownames(mgas_df) <- make.names(mgas_df$sysName, unique=TRUE)

## Error in make.names(mgas_df$sysName, unique = TRUE): object 'mgas_df' not found

## First make a template configuration
circos_test <- circos_prefix()

## This assumes you have a colors.conf in circos/colors/ and fonts.conf in circos/fonts/

## It also assumes you have conf/ideogram.conf, conf/ticks.conf, and conf/housekeeping.conf

## It will write circos/conf/mgas.conf with a reasonable first approximation config file.

## Creating the data directory: circos/data

## The circos directory does not exist, creating: circos/conf

## The karyotype directory does not exist, creating: circos/conf/karyotypes

## The ideogram directory does not exist, creating: circos/conf/ideograms

## Wrote karyotype to 5

## This should match the karyotype= line in mgas.conf

## Fill it in with the data for s.pyogenes
circos_kary <- circos_karyotype("mgas", length=1895017)

## Wrote karyotype to circos/conf/karyotypes/mgas.conf
## This should match the karyotype= line in mgas.conf

## Fill in the gene category annotations by gene-strand
circos_plus <- circos_plus_minus(mgas_df, circos_test)

## Error in circos_plus_minus(mgas_df, circos_test): object 'mgas_df' not found

circos_limma_hist <- circos_hist(spyogenes_de$limma$all_tables[[1]], mgas_df, circos_test, outer=circos_plus)

## Error in as.data.frame(y): object 'mgas_df' not found

circos_deseq_hist <- circos_hist(spyogenes_de$deseq$all_tables[[1]], mgas_df, circos_test, outer=circos_limma_hist)

## Error in as.data.frame(y): object 'mgas_df' not found

circos_edger_hist <- circos_hist(spyogenes_de$edger$all_tables[[1]], mgas_df, circos_test, outer=circos_deseq_hist)

## Error in as.data.frame(y): object 'mgas_df' not found

circos_suffix(cfgout=circos_test)
circos_made <- circos_make(target="mgas")
## For some reason this fails weirdly when not run interactively.
getwd()

## [1] "/cbcb/nelsayed-scratch/atb/git/hpgltools/vignettes"

circos result

GenoplotR is new to me, but it seems to work?

genoplot_chromosome()

## Warning in strings[i] <- downloaded: number of items to replace is not a
## multiple of replacement length

## Warning: closing unused connection 5 (circos/Makefile)

index.html

pander::pander(sessionInfo())

R version 3.4.1 (2017-06-30)

**Platform:** x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_US.utf8, LC_NUMERIC=C, LC_TIME=en_US.utf8, LC_COLLATE=en_US.utf8, LC_MONETARY=en_US.utf8, LC_MESSAGES=en_US.utf8, LC_PAPER=en_US.utf8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.utf8 and LC_IDENTIFICATION=C

attached base packages: stats, graphics, grDevices, utils, datasets and base

other attached packages: ruv(v.0.9.6) and hpgltools(v.2017.01)

loaded via a namespace (and not attached): nlme(v.3.1-131), bitops(v.1.0-6), matrixStats(v.0.52.2), devtools(v.1.13.2), bit64(v.0.9-7), doParallel(v.1.0.10), RColorBrewer(v.1.1-2), rprojroot(v.1.2), GenomeInfoDb(v.1.12.2), tools(v.3.4.1), backports(v.1.1.0), R6(v.2.2.2), rpart(v.4.1-11), KernSmooth(v.2.23-15), Hmisc(v.4.0-3), DBI(v.0.7), lazyeval(v.0.2.0), BiocGenerics(v.0.22.0), mgcv(v.1.8-17), colorspace(v.1.3-2), ade4(v.1.7-6), nnet(v.7.3-12), withr(v.1.0.2), RMySQL(v.0.10.12), gridExtra(v.2.2.1), DESeq2(v.1.16.1), bit(v.1.1-12), compiler(v.3.4.1), preprocessCore(v.1.38.1), Biobase(v.2.36.2), htmlTable(v.1.9), DelayedArray(v.0.2.7), labeling(v.0.3), checkmate(v.1.8.3), scales(v.0.4.1), DEoptimR(v.1.0-8), robustbase(v.0.92-7), genefilter(v.1.58.1), stringr(v.1.2.0), digest(v.0.6.12), foreign(v.0.8-69), rmarkdown(v.1.6), XVector(v.0.16.0), base64enc(v.0.1-3), htmltools(v.0.3.6), limma(v.3.32.4), highr(v.0.6), htmlwidgets(v.0.9), rlang(v.0.1.1), RSQLite(v.2.0), BiocInstaller(v.1.26.0), BiocParallel(v.1.10.1), gtools(v.3.5.0), acepack(v.1.4.1), RCurl(v.1.95-4.8), magrittr(v.1.5), GenomeInfoDbData(v.0.99.0), Formula(v.1.2-2), Matrix(v.1.2-10), Rcpp(v.0.12.12), munsell(v.0.4.3), S4Vectors(v.0.14.3), stringi(v.1.1.5), yaml(v.2.1.14), edgeR(v.3.18.1), SummarizedExperiment(v.1.6.3), zlibbioc(v.1.22.0), Rtsne(v.0.13), plyr(v.1.8.4), grid(v.3.4.1), blob(v.1.1.0), parallel(v.3.4.1), ggrepel(v.0.6.5), crayon(v.1.3.2), genoPlotR(v.0.8.6), methods(v.3.4.1), lattice(v.0.20-35), splines(v.3.4.1), pander(v.0.6.0), annotate(v.1.54.0), locfit(v.1.5-9.1), knitr(v.1.16), GenomicRanges(v.1.28.4), corpcor(v.1.6.9), geneplotter(v.1.54.0), reshape2(v.1.4.2), codetools(v.0.2-15), stats4(v.3.4.1), XML(v.3.98-1.9), evaluate(v.0.10.1), latticeExtra(v.0.6-28), data.table(v.1.10.4), foreach(v.1.4.3), testthat(v.1.0.2), gtable(v.0.2.0), ggplot2(v.2.2.1), openxlsx(v.4.0.17), xtable(v.1.8-2), survival(v.2.41-3), tibble(v.1.3.3), iterators(v.1.0.8), AnnotationDbi(v.1.38.2), memoise(v.1.1.0), IRanges(v.2.10.2), cluster(v.2.0.6) and sva(v.3.24.4)